easyalluvial

# install.packages('easyalluvial')
suppressPackageStartupMessages(require(tidyverse))
suppressPackageStartupMessages(require(easyalluvial))

RColorBrewer

How to expand color palette with ggplot and RColorBrewer

https://www.r-bloggers.com/how-to-expand-color-palette-with-ggplot-and-rcolorbrewer/

highcharter

http://jkunst.com/highcharter/

https://github.com/jbkunst/highcharter

http://www.htmlwidgets.org/index.html

https://cran.r-project.org/web/packages/highcharter/index.html

https://www.datacamp.com/community/tutorials/data-visualization-highcharter-r

library(tidyverse)
library(highcharter)
## Highcharts (www.highcharts.com) is a Highsoft software product which is
## not free for commercial and Governmental use
data("pokemon")
# glimpse(pokemon)
hchart works like ggplot2's qplot.
hc_add_series works like ggplot2's geom_S.
hcaes works like ggplot2's aes.
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n"))
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "column", hcaes(x = "type_1", y = "n"))
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "treemap", hcaes(x = "type_1", value = "n", color = "n"))
highchart() %>%
  hc_add_series(pokemon, "scatter", hcaes(x = "height", y = "weight"))
data(diamonds, package = "ggplot2")

set.seed(123)
data <- sample_n(diamonds, 300)

modlss <- loess(price ~ carat, data = data)
fit <- arrange(broom::augment(modlss), carat)

highchart() %>%
  hc_add_series(data, type = "scatter",
                hcaes(x = "carat", y = "price", size = "depth", group = "cut")) %>%
  hc_add_series(fit, type = "line", hcaes(x = "carat", y = ".fitted"),
                name = "Fit", id = "fit") %>%
  hc_add_series(fit, type = "arearange",
                hcaes(x = "carat", low = ".fitted - 2*.se.fit",
                      high = ".fitted + 2*.se.fit"),
                linkedTo = "fit")
highchart() %>%
  hc_chart(type = "area") %>%
  hc_title(text = "Historic and Estimated Worldwide Population Distribution by Region") %>%
  hc_subtitle(text = "Source: Wikipedia.org") %>%
  hc_xAxis(categories = c("1750", "1800", "1850", "1900", "1950", "1999", "2050"),
           tickmarkPlacement = "on",
           title = list(enabled = FALSE)) %>%
  hc_yAxis(title = list(text = "Percent")) %>%
  hc_tooltip(pointFormat = "<span style=\"color:{series.color}\">{series.name}</span>:
             <b>{point.percentage:.1f}%</b> ({point.y:,.0f} millions)<br/>",
             shared = TRUE) %>%
  hc_plotOptions(area = list(
     stacking = "percent",
     lineColor = "#ffffff",
     lineWidth = 1,
     marker = list(
       lineWidth = 1,
       lineColor = "#ffffff"
       ))
     ) %>%
  hc_add_series(name = "Asia", data = c(502, 635, 809, 947, 1402, 3634, 5268)) %>%
  hc_add_series(name = "Africa", data = c(106, 107, 111, 133, 221, 767, 1766)) %>%
  hc_add_series(name = "Europe", data = c(163, 203, 276, 408, 547, 729, 628)) %>%
  hc_add_series(name = "America", data = c(18, 31, 54, 156, 339, 818, 1201)) %>%
  hc_add_series(name = "Oceania", data = c(2, 2, 2, 6, 13, 30, 46))
x <- quantmod::getSymbols("GOOG", auto.assign = FALSE)
## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
## 
## WARNING: There have been significant changes to Yahoo Finance data.
## Please see the Warning section of '?getSymbols.yahoo' for details.
## 
## This message is shown once per session and may be disabled by setting
## options("getSymbols.yahoo.warning"=FALSE).
hchart(x)
y <- quantmod::getSymbols("AMZN", auto.assign = FALSE)

highchart(type = "stock") %>%
  hc_add_series(x) %>%
  hc_add_series(y, type = "ohlc")

Highmaps - Map Collection
https://code.highcharts.com/mapdata/

hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js")%>%
  hc_title(text = "India")
hcmap("https://code.highcharts.com/mapdata/countries/tr/tr-all.js")%>%
  hc_title(text = "Turkey")
download_map_data: Download the geojson data from the highcharts collection.
get_data_from_map: Get the properties for each region in the map, as the keys from the map data.
mapdata <- get_data_from_map(download_map_data("https://code.highcharts.com/mapdata/countries/in/in-all.js"))
# glimpse(mapdata)
#population state wise
pop <-  as.data.frame(c(84673556, 1382611, 31169272, 103804637, 1055450, 25540196, 342853, 242911, 18980000, 1457723, 60383628, 25353081, 6864602,
12548926, 32966238, 61130704, 33387677, 64429, 72597565, 112372972, 2721756, 2964007, 1091014, 1980602, 41947358, 1244464,
27704236, 68621012, 607688, 72138958, 3671032, 207281477, 10116752,91347736))

state <-  mapdata%>%
  select(`hc-a2`)%>%
  arrange(`hc-a2`)

State_pop <-  as.data.frame(c(state, pop))
names(State_pop)= c("State", "Population")

hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js", data = State_pop, value = "Population",
      joinBy = c("hc-a2", "State"), name = "Fake data",
      dataLabels = list(enabled = TRUE, format = '{point.name}'),
      borderColor = "#FAFAFA", borderWidth = 0.1,
      tooltip = list(valueDecimals = 0))
data(mpg, package = "ggplot2")

mpgg <- mpg %>%
  filter(class %in% c("suv", "compact", "midsize")) %>%
  group_by(class, manufacturer) %>%
  summarize(count = n())

categories_grouped <- mpgg %>%
  group_by(name = class) %>%
  do(categories = .$manufacturer) %>%
  list_parse()

highchart() %>%
  hc_xAxis(categories = categories_grouped) %>%
  hc_add_series(data = mpgg, type = "bar", hcaes(y = "count", color = "manufacturer"),
                showInLegend = FALSE)
df <- data_frame(
  name = c("Animals", "Fruits", "Cars"),
  y = c(5, 2, 4),
  drilldown = tolower(name)
)
## Warning: `data_frame()` is deprecated, use `tibble()`.
## This warning is displayed once per session.
ds <- list_parse(df)
names(ds) <- NULL


hc <- highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Basic drilldown") %>%
  hc_xAxis(type = "category") %>%
  hc_legend(enabled = FALSE) %>%
  hc_plotOptions(
    series = list(
      boderWidth = 0,
      dataLabels = list(enabled = TRUE)
    )
  ) %>%
  hc_add_series(
    name = "Things",
    colorByPoint = TRUE,
    data = ds
  )

dfan <- data_frame(
  name = c("Cats", "Dogs", "Cows", "Sheep", "Pigs"),
  value = c(4, 3, 1, 2, 1)
)

dffru <- data_frame(
  name = c("Apple", "Organes"),
  value = c(4, 2)
)

dfcar <- data_frame(
  name = c("Toyota", "Opel", "Volkswage"),
  value = c(4, 2, 2)
)

second_el_to_numeric <- function(ls){

  map(ls, function(x){
    x[[2]] <- as.numeric(x[[2]])
    x
  })

}

dsan <- second_el_to_numeric(list_parse2(dfan))

dsfru <- second_el_to_numeric(list_parse2(dffru))

dscar <- second_el_to_numeric(list_parse2(dfcar))

hc %>%
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series = list(
      list(
        id = "animals",
        data = dsan
      ),
      list(
        id = "fruits",
        data = dsfru
      ),
      list(
        id = "cars",
        data = dscar
      )
    )
  )
tm <- pokemon %>%
  mutate(type_2 = ifelse(is.na(type_2), paste("only", type_1), type_2),
         type_1 = type_1) %>%
  group_by(type_1, type_2) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  treemap::treemap(index = c("type_1", "type_2"),
                   vSize = "n", vColor = "type_1")

tm$tm <- tm$tm %>%
  tbl_df() %>%
  left_join(pokemon %>% select(type_1, type_2, color_f) %>% distinct(), by = c("type_1", "type_2")) %>%
  left_join(pokemon %>% select(type_1, color_1) %>% distinct(), by = c("type_1")) %>%
  mutate(type_1 = paste0("Main ", type_1),
         color = ifelse(is.na(color_f), color_1, color_f))
## Warning: Column `type_1` joining factor and character vector, coercing into
## character vector
## Warning: Column `type_2` joining factor and character vector, coercing into
## character vector
highchart() %>%
  hc_add_series_treemap(tm, allowDrillToNode = TRUE,
                        layoutAlgorithm = "squarified")
## Warning: 'hc_add_series_treemap' is deprecated.
## Use 'hctreemap' instead.
## See help("Deprecated")
pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)
pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)%>%
hc_add_theme(hc_theme_chalk())
data("weather")

x <- c("Min", "Mean", "Max")
y <- sprintf("{point.%s}", c("min_temperaturec", "mean_temperaturec", "max_temperaturec"))
tltip <- tooltip_table(x, y)

hchart(weather, type = "columnrange",
       hcaes(x = "date", low = "min_temperaturec", high = "max_temperaturec",
             color = "mean_temperaturec")) %>%
  hc_chart(polar = TRUE) %>%
  hc_yAxis( max = 30, min = -10, labels = list(format = "{value} C"),
            showFirstLabel = FALSE) %>%
  hc_xAxis(
    title = list(text = ""), gridLineWidth = 0.5,
    labels = list(format = "{value: %b}")) %>%
  hc_tooltip(useHTML = TRUE, pointFormat = tltip,
             headerFormat = as.character(tags$small("{point.x:%d %B, %Y}")))

taucharts

https://www.infoworld.com/video/87337/r-tip-how-to-create-easy-interactive-scatter-plots-with-taucharts

devtools::install_github("hrbrmstr/taucharts")
## Skipping install of 'taucharts' from a github remote, the SHA1 (93dbdce5) has not changed since last install.
##   Use `force = TRUE` to force installation
# githubinstall::githubinstall("taucharts")
library(tidyverse)
library(taucharts)
data("mtcars")
mtcars2 <- mtcars %>% 
  select(wt, mpg) %>% 
  mutate(model = row.names(mtcars))
taucharts::tauchart(mtcars2) %>% 
  tau_point(x = "wt", y = "mpg") %>% 
  tau_tooltip() %>% 
  tau_trendline()

gganimate

https://www.infoworld.com/video/89987/r-tip-animations-in-r

ggplot2

http://r-statistics.co/ggplot2-Tutorial-With-R.html

library(ggplot2)
diamonds
ggplot(diamonds)  # if only the dataset is known.

ggplot(diamonds, aes(x=carat))  # if only X-axis is known. The Y-axis can be specified in respective geoms.

ggplot(diamonds, aes(x=carat, y=price))  # if both X and Y axes are fixed for all layers.

ggplot(diamonds, aes(x=carat, color=cut))  # Each category of the 'cut' variable will now have a distinct  color, once a geom is added.

ggplot(diamonds, aes(x=carat), color="steelblue")

ggplot(diamonds, aes(x=carat, y=price, color=cut)) + 
  geom_point() + 
  geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# Adding scatterplot geom (layer1) and smoothing geom (layer2).
ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price, color=cut))
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# Same as above but specifying the aesthetics inside the geoms.
library(ggplot2)
ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price)) # Remove color from geom_smooth
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(diamonds, aes(x=carat, y=price)) + 
  geom_point(aes(color=cut)) + 
  geom_smooth()  # same but simpler
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

continue from here http://r-statistics.co/ggplot2-Tutorial-With-R.html

gganimate

https://cran.r-project.org/web/packages/gganimate/vignettes/gganimate.html

library(gganimate)
#> Loading required package: ggplot2

# We'll start with a static plot
p <- ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) + 
  geom_point()

plot(p)

anim <- p + 
  transition_states(Species,
                    transition_length = 2,
                    state_length = 1)

anim

anim + 
  ease_aes('cubic-in-out') # Slow start and end for a smoother look

anim + 
  ease_aes('cubic-in-out',
           y = 'bounce-out') # Sets special ease for y aesthetic

anim + 
  ggtitle('Now showing {closest_state}',
          subtitle = 'Frame {frame} of {nframes}')

ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) + 
  geom_line(aes(group = rep(1:50, 3)), colour = 'grey') + 
  geom_point()

ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) + 
  geom_point(aes(colour = Species)) + 
  transition_states(Species,
                    transition_length = 2,
                    state_length = 1)